Bibliographic record
Abstract
Sometimes dramatically changing vogues in diagnostic practice in psychiatry resemble the volatility of international share markets. One such quickly shifting diagnostic area has been that of bipolar disorder (BD). Historically regarded as a relatively uncommon condition until recent decades, the construct of BD underwent a major expansion in the 1990s and 2000s with promulgation of the concept of the soft bipolar spectrum disorder, from which the recent research focus on subthreshold BD presentations was derived. Related to this has been renewed interest in treatments for BD from the pharmaceutical industry. The increasing rates of diagnosis have largely related to BD II, for which there has been a dramatic broadening of diagnostic criteria. This article critically reviews research data, both for broadening the diagnostic criteria for BD and, conversely, for the growing evidence of overdiagnosis in clinical practice. Why does this debate matter? I would suggest that there are many valid reasons to be concerned about overdiagnosis: first, the potential for overtreatment or inappropriate treatment of such patients with mood stabilizing treatments, including antipsychotics; second, the potential for diagnostic oversimplification, with consequent diagnostic deskilling and loss of credibility for the psychiatric profession; and third, the potential major impact on etiologic research for this condition. Psychiatry should not uncritically accept the shift to overdiagnosis, which has developed a rapid momentum in recent decades, in both clinical and academic circles. We must ensure, as a profession, that any change in diagnostic practice is underpinned by rigorous and critical research inquiry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".